Scott Alfeld
Impact in
- Artificial Intelligence top 10%
- Adversarial Robustness in Machine Learning
- Privacy-Preserving Technologies in Data
- Anomaly Detection Techniques and Applications
- Signal Processing top 10%
- Advanced Malware Detection Techniques
Papers in
-
- Adversarial Robustness in Machine Learning 5
- Anomaly Detection Techniques and Applications 2
- Data Stream Mining Techniques 1
- Bayesian Modeling and Causal Inference 1
- Co-authors
- Paul Barford (5 shared papers)Xiaojin Zhu (2 shared papers)Benjamin I. P. Rubinstein (2 shared papers)S. Muthukrishnan (1 shared paper)Carol Barford (1 shared paper)Yevgeniy Vorobeychik (2 shared papers)Will Fleisher (1 shared paper)Wheeler Ruml (1 shared paper)
- Journals
- ACM Transactions on Knowledge Discovery from Data (1 paper)arXiv (Cornell University) (1 paper)Proceedings of the AAAI Conference on Artificial Intelligence (5 papers)SSRN Electronic Journal (1 paper)
- Partner nations
- United StatesAustraliaMexico
In The Last Decade
Scott Alfeld
11 papers receiving 219 citations
Peers
Comparison fields: 5 of 48
- Artificial Intelligence 166
- Signal Processing 46
- Computer Science Applications 22
- Health Informatics 4
- Information Systems 45
Countries citing papers authored by Scott Alfeld
This map shows the geographic impact of Scott Alfeld's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Scott Alfeld with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Scott Alfeld more than expected).
Fields of papers citing papers by Scott Alfeld
This network shows the impact of papers produced by Scott Alfeld. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Scott Alfeld. The network helps show where Scott Alfeld may publish in the future.
Co-authors
The 11 scholars most cited alongside Scott Alfeld, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2016 | 116 | |
| 2 | 2016 | 53 | |
| 3 | 2022 | 37 | |
| 4 | 2017 | 12 | |
| 5 | 2018 | 3 | |
| 6 | 2012 | 2 | |
| 7 | 2019 | 2 | |
| 8 | 2023 | 1 | |
| 9 | 2023 | 1 | |
| 10 | 2021 | 1 | |
| 11 | 2014 | 1 |
About Scott Alfeld
Scott Alfeld is a scholar working on Artificial Intelligence, Computer Networks and Communications, Sociology and Political Science, Information Systems and Statistical and Nonlinear Physics, having authored 11 papers that have together received 229 indexed citations. Recurring topics across this work include Adversarial Robustness in Machine Learning (5 papers), Complex Network Analysis Techniques (2 papers), Spam and Phishing Detection (2 papers), Electric Power System Optimization (2 papers), Anomaly Detection Techniques and Applications (2 papers), Qualitative Comparative Analysis Research (1 paper), Data Stream Mining Techniques (1 paper) and Bayesian Modeling and Causal Inference (1 paper). The work is most often cited by research in Artificial Intelligence (166 citations), Signal Processing (46 citations), Computer Science Applications (22 citations), Health Informatics (4 citations) and Information Systems (45 citations). Scott Alfeld has collaborated with scholars based in United States, Australia and Mexico. Frequent co-authors include Paul Barford, Xiaojin Zhu, Benjamin I. P. Rubinstein, S. Muthukrishnan, Carol Barford, Yevgeniy Vorobeychik, Will Fleisher, Wheeler Ruml, Tina Eliassi‐Rad and David Liu. Their work appears in journals such as ACM Transactions on Knowledge Discovery from Data, arXiv (Cornell University), Proceedings of the AAAI Conference on Artificial Intelligence and SSRN Electronic Journal.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.